




"""simple docstring"""






from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowercase__			=		logging.get_logger(__name__)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				try:
								with open(UpperCamelCase__				,       'rb'						) as flax_state_f:
												_lowerCamelCase	:     Union[str, Any]       =       from_bytes(UpperCamelCase__				,       flax_state_f.read()						)
				except UnpicklingError as e:
								try:
												with open(UpperCamelCase__						) as f:
																if f.read().startswith('version'						):
																				raise OSError(
																				    'You seem to have cloned a repository without having git-lfs installed. Please'
																				    ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
																				    ' folder you cloned.'						)
																else:
																				raise ValueError from e
								except (UnicodeDecodeError, ValueError):
												raise EnvironmentError(f'''Unable to convert {model_file} to Flax deserializable object. '''						)

				return load_flax_weights_in_pytorch_model(UpperCamelCase__				,       UpperCamelCase__						)







def 	_snake_case     (							lowercase__				,       lowercase__						):

				try:
								import torch  # noqa: F401
				except ImportError:
								logger.error(
								    'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
								    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
								    ' instructions.'						)
								raise

				# check if we have bf16 weights
				_lowerCamelCase	:     Union[str, Any]       =       flatten_dict(jax.tree_util.tree_map(lambda lowercase__						: x.dtype == jnp.bfloataa				,       UpperCamelCase__						)						).values()
				if any(UpperCamelCase__						):
								# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

								# and bf16 is not fully supported in PT yet.
								logger.warning(
								    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
								    'before loading those in PyTorch model.'						)
								_lowerCamelCase	:     List[str]       =       jax.tree_util.tree_map(
								    lambda lowercase__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params				,       UpperCamelCase__						)

				_lowerCamelCase	:     List[str]       =       ''

				_lowerCamelCase	:     List[str]       =       flatten_dict(UpperCamelCase__				,       sep='.'						)
				_lowerCamelCase	:     Any       =       pt_model.state_dict()

				# keep track of unexpected & missing keys
				_lowerCamelCase	:     int       =       []
				_lowerCamelCase	:     str       =       set(pt_model_dict.keys()						)

				for flax_key_tuple, flax_tensor in flax_state_dict.items():
								_lowerCamelCase	:     str       =       flax_key_tuple.split('.'						)

								if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
												_lowerCamelCase	:     int       =       flax_key_tuple_array[:-1] + ['weight']
												_lowerCamelCase	:     Any       =       jnp.transpose(UpperCamelCase__				,       (3, 2, 0, 1)						)
								elif flax_key_tuple_array[-1] == "kernel":
												_lowerCamelCase	:     Optional[int]       =       flax_key_tuple_array[:-1] + ['weight']
												_lowerCamelCase	:     List[str]       =       flax_tensor.T
								elif flax_key_tuple_array[-1] == "scale":
												_lowerCamelCase	:     Optional[int]       =       flax_key_tuple_array[:-1] + ['weight']

								if "time_embedding" not in flax_key_tuple_array:
												for i, flax_key_tuple_string in enumerate(UpperCamelCase__						):
																_lowerCamelCase	:     Any       =       (
																    flax_key_tuple_string.replace('_0'				,       '.0'						)
																    .replace('_1'				,       '.1'						)
																    .replace('_2'				,       '.2'						)
																    .replace('_3'				,       '.3'						)
																    .replace('_4'				,       '.4'						)
																    .replace('_5'				,       '.5'						)
																    .replace('_6'				,       '.6'						)
																    .replace('_7'				,       '.7'						)
																    .replace('_8'				,       '.8'						)
																    .replace('_9'				,       '.9'						)
																)

								_lowerCamelCase	:     Union[str, Any]       =       '.'.join(UpperCamelCase__						)

								if flax_key in pt_model_dict:
												if flax_tensor.shape != pt_model_dict[flax_key].shape:
																raise ValueError(
																    f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																    f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)
												else:
																# add weight to pytorch dict
																_lowerCamelCase	:     List[str]       =       np.asarray(UpperCamelCase__						) if not isinstance(UpperCamelCase__				,       np.ndarray						) else flax_tensor
																_lowerCamelCase	:     List[Any]       =       torch.from_numpy(UpperCamelCase__						)
																# remove from missing keys
																missing_keys.remove(UpperCamelCase__						)
								else:
												# weight is not expected by PyTorch model
												unexpected_keys.append(UpperCamelCase__						)

				pt_model.load_state_dict(UpperCamelCase__						)

				# re-transform missing_keys to list
				_lowerCamelCase	:     List[str]       =       list(UpperCamelCase__						)

				if len(UpperCamelCase__						) > 0:
								logger.warning(
								    'Some weights of the Flax model were not used when initializing the PyTorch model'
								    f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
								    f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
								    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
								    f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
								    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
								    ' FlaxBertForSequenceClassification model).'						)
				if len(UpperCamelCase__						) > 0:
								logger.warning(
								    f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
								    f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
								    ' use it for predictions and inference.'						)

				return pt_model
'''simple docstring'''




from manim import *




class        a__ (  UpperCAmelCase__					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    Rectangle(height=0.5    ,							width=0.5       )
         __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0       )

         __lowerCamelCase            =    Rectangle(height=0.25    ,							width=0.25       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''CPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         cpu.move_to([-2.5, -0.5, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(4       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''GPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         gpu.move_to([-1, -1, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )

         __lowerCamelCase            =    Text('''Model'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         model.move_to([3, -1.0, 0]       )
         self.add(a       )

         __lowerCamelCase            =    []
         __lowerCamelCase            =    []

         for i, rect in enumerate(a       ):
             __lowerCamelCase            =    fill.copy().set_fill(a    ,							opacity=0.8       )
             target.move_to(a       )
             model_arr.append(a       )

             __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0.0       ).set_fill(a    ,							opacity=0.8       )
             cpu_target.move_to(cpu_left_col_base[i]       )
             model_cpu_arr.append(a       )

         self.add(*a    ,							*a       )

         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''Disk'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         disk.move_to([-4, -1.25, 0]       )
         self.add(a    ,							a       )

         __lowerCamelCase            =    Square(side_length=2.2       )
         key.move_to([-5, 2, 0]       )

         __lowerCamelCase            =    MarkupText(
             f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""    ,							font_size=18    ,							)

         key_text.move_to([-5, 2.4, 0]       )

         self.add(a    ,							a       )

         __lowerCamelCase            =    MarkupText(
             f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""    ,							font_size=18    ,							)

         blue_text.next_to(a    ,							DOWN * 2.4    ,							aligned_edge=key_text.get_left()       )
         self.add(a       )

         __lowerCamelCase            =    MarkupText(
             f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a       )       )

         __lowerCamelCase            =    Square(0.3       )
         input.set_fill(a    ,							opacity=1.0       )
         input.set_stroke(width=0.0       )
         input.next_to(model_base[0]    ,							a    ,							buff=0.5       )

         self.play(Write(a       )       )

         input.generate_target()
         input.target.next_to(model_arr[0]    ,							direction=a    ,							buff=0.02       )
         self.play(MoveToTarget(a       )       )

         self.play(FadeOut(a       )       )

         __lowerCamelCase            =    Arrow(start=a    ,							end=a    ,							color=a    ,							buff=0.5       )
         a.next_to(model_arr[0].get_left()    ,							a    ,							buff=0.2       )

         model_cpu_arr[0].generate_target()
         model_cpu_arr[0].target.move_to(gpu_rect[0]       )

         __lowerCamelCase            =    MarkupText(
             f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )       )

         __lowerCamelCase            =    {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}

         self.play(
             Write(a       )    ,							Circumscribe(model_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_cpu_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)
         self.play(MoveToTarget(model_cpu_arr[0]       )       )

         __lowerCamelCase            =    a.copy()
         for i in range(6       ):
             a_c.next_to(model_arr[i].get_right() + 0.02    ,							a    ,							buff=0.2       )

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02       )

             __lowerCamelCase            =    AnimationGroup(
                 FadeOut(a    ,							run_time=0.5       )    ,							MoveToTarget(a    ,							run_time=0.5       )    ,							FadeIn(a    ,							run_time=0.5       )    ,							lag_ratio=0.2       )

             self.play(a       )

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]       )

             if i < 5:
                 model_cpu_arr[i + 1].generate_target()
                 model_cpu_arr[i + 1].target.move_to(gpu_rect[0]       )
                 if i >= 1:
                     __lowerCamelCase            =    0.7

                 self.play(
                     Circumscribe(model_arr[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i + 1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_arr[i + 1]    ,							color=a    ,							**a       )    ,							)
                 if i < 1:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]       )    ,							MoveToTarget(model_cpu_arr[i + 1]       )    ,							)
                 else:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]    ,							run_time=0.7       )    ,							MoveToTarget(model_cpu_arr[i + 1]    ,							run_time=0.7       )    ,							)
             else:
                 model_cpu_arr[i].generate_target()
                 model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]       )
                 input.generate_target()
                 input.target.next_to(model_arr[-1].get_right()    ,							RIGHT + 0.02    ,							buff=0.2       )

                 self.play(
                     Circumscribe(model_arr[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(cpu_left_col_base[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)

                 self.play(MoveToTarget(model_cpu_arr[i]       )       )

             __lowerCamelCase            =    a_c
             __lowerCamelCase            =    a_c.copy()

         input.generate_target()
         input.target.next_to(model_base[-1]    ,							RIGHT + 0.02    ,							buff=0.5       )
         self.play(
             FadeOut(a       )    ,							FadeOut(a    ,							run_time=0.5       )    ,							)

         __lowerCamelCase            =    MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""    ,							font_size=24       )
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )    ,							MoveToTarget(a       )       )

         self.wait()
